When writing our implementation and subsequently deploying it during the competition, we made a number of essentially ad hoc decisions.
First, we included an estimate of [1]}'s covariate balancing propensity score (CBPS) as an additional covariate in the expected outcome model.
Second, in our regression tree prior, we selected splitting variables using [2]}'s sparsity-inducing Dirichlet prior instead of the uniform prior that is used in the bcf package.
Briefly, [2]}'s prior encourages regression trees to split on a small number of covariates, thereby facilitating a type of automatic variable selection.
In the context of treatment effect estimation, such a sparsity-inducing prior can potentially identify the main drivers of effect heterogeneity.
We assess the sensitivity of our contest submissions to these modeling choices.
Specifically, we study the extent to which our results change when we use different estimates of the propensity score and when we use [4]}'s uniform prior on the splitting variables in the trees.
